ULC = Unique Link Clicks (ULC),
CTR = Click-Through Rate
(CTR in %),
uCTR = Unique Click-Through Rate (Unique CTR in %),
amount_spent = Amount Spent in \(,<br>
CPC = Cost Per Click (CPC),<br>
CPR = Cost per Result (CPR))<br> Group 1 includes Australia,
Canada,
United Kingdom, Ghana, Nigeria, Pakistan, United States.<br> Group
2
includes Australia, Canada, United Kingdom, Ghana, Niger, Nigeria,
Nepal, Pakistan, Thailand, Taiwan.
</td>
</tr>
</tfoot>
</table>
</div>
</div>
<div id="initial-analysis" class="section
level2">
<h2>Initial Analysis</h2>
<p>Performing a preliminary analysis of the dataset first, to
discover
trends and relationships, and familiarize ourselves with the dataset
using R. <br></p>
<div id="bar-charts" class="section level3">
<h3>Bar Charts</h3>
<p>Looking at our dataset,specific ad campaigns are targeted
towards
specific audience. Let’s analyse individual campaigns to see how they
are peforming.</p>
<div class="sourceCode" id="cb4"><pre
class="sourceCode r"><code class="sourceCode
r"><span id="cb4-1"><a
href="#cb4-1"
tabindex="-1"></a>amountspent_campaign <span
class="ot"><-</span>
campaign_clean</span>
<span id="cb4-2"><a href="#cb4-2"
tabindex="-1"></a></span>
<span id="cb4-3"><a href="#cb4-3"
tabindex="-1"></a>amountspent_campaign <span
class="sc">|></span></span>
<span id="cb4-4"><a href="#cb4-4"
tabindex="-1"></a> <span
class="fu">ggplot</span>(<span
class="fu">aes</span>(<span
class="at">x =</span> <span
class="fu">reorder</span>(campaign_id,<span
class="sc">-</span>amount_spent), <span
class="at">y =</span> amount_spent))<span
class="sc">+</span></span>
<span id="cb4-5"><a href="#cb4-5"
tabindex="-1"></a> <span
class="fu">geom_col</span>(<span
class="at">width =</span> <span
class="fl">0.9</span>, <span
class="at">fill =</span> <span
class="st">"#09979B"</span>)<span
class="sc">+</span></span>
<span id="cb4-6"><a href="#cb4-6"
tabindex="-1"></a> <span
class="fu">geom_text</span>(<span
class="fu">aes</span>(<span
class="at">label =</span> <span
class="fu">paste0</span>(<span
class="st">"\)",scales::comma(amount_spent,accuracy = 0.01))),
family = "Century Gothic",
fontface = "bold", vjust = -0.5, size = 5)+ scale_y_continuous(expand =
c(0,1), limits
= nicelimits)+ scale_x_discrete(label =
scales::wrap_format(width = 10))+ labs(x = NULL, y = "Amount Spent", title = "Amount Spent on Campaigns")+ theme(text = element_text(family = "Century Gothic", face = "bold", size = 14, color = "black"), panel.background = element_blank(), panel.grid.major.y = element_line(colour = "gray",size = 0.25), axis.text = element_text(size = 11, colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
From the above chart chart, we can determine that the Campaign 1
(Educators and Principals) campaign has been the most expensive, whereas
the campaign targeting the students of Ghana (Campaign 5) has been the
least expensive. But if we see a comparison of Campaign with Reach, we
notice something peculiar:
reach_campaign <- campaign_clean
reach_campaign |>
ggplot(aes(x = reorder(campaign_id,-Reach), y = Reach))+
geom_col(width = 0.9, fill = "#27A0CC")+
geom_text(aes(label = scales::comma(Reach), vjust = -0.5), family = "Century Gothic", fontface = "bold", size = 5)+
scale_y_continuous(expand = c(0,0), limits = nicelimits)+
scale_x_discrete(label = scales::wrap_format(width = 10))+
labs(x=NULL, title = "Reach Across Campaigns")+
theme(text = element_text(family = "Century Gothic", face = "bold", size = 14, color = "black"),
panel.background = element_blank(),
panel.grid.major.y = element_line(colour = "gray",size = 0.25),
axis.text = element_text(size = 11, colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
Campaign 1, The Educators and Principals campaign is not the campaign
with the highest reach. It isn’t even the second-highest reach – it
comes at the fourth position. Whereas the inexpensive Campaing 5 , The
Ghana campaign is in the 6th position, showing more reach with low cost.
Let’s explore this further;
geo_cpc <- campaign_clean
geo_cpc |>
ggplot(aes(x=reorder(Geography,CPC), y=CPC, fill = CPC))+
geom_col(width = 0.8)+
scale_fill_gradient2(high = "#2ECC71",mid = "#F1C40F", low = "#E74C3C", midpoint = median(campaign_clean$CPC,na.rm = TRUE))+
coord_flip(clip = "off")+
labs(x=NULL, y = "Cost Per Click", title = "Cost Per Click Across Geographic locations")+
geom_text(aes(label = paste0(round(CPC,2),"$")), hjust =-0.1, family = "Century Gothic", size = 5, fontface="bold")+
scale_y_continuous(expand = c(0,0),limits = nicelimits)+
theme(text = element_text(family = "Century Gothic", size = 15, face = "bold", colour = "black"),
axis.text = element_text(family = "Century Gothic", size = 15, face = "bold", colour = "black"),
panel.background = element_blank(),
panel.grid.major.x = element_line(colour = "gray",size = 0.25),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 15))
Nigeria has some of the lowest CPC, but India, which had a more
successful ad campaign in terms of reach, also has a low CPC.
Scatter/Bubble plots
A bubble plot to explore the relationship between Reach and Impressions
reach_imp <- campaign_clean
reach_imp|>
ggplot(aes(x=Reach, y = Impressions, size = ULC, colour = campaign_id))+
geom_point(alpha = 0.7, show.legend = c(colour = FALSE))+
geom_text(aes(label = if_else(Reach > 5000, str_wrap(campaign_id,width = 15),"")),
size = 4.5, colour = "black", vjust = -2.5,family = "Century Gothic", fontface = "bold")+
scale_size_continuous(range = c(4,20))+
scale_x_continuous(limits = nicelimits)+
scale_y_continuous(limits = c(0,80000)) +
labs(x = "Reach (Unique Users)", y = "Impressions (Total Views) ", size = "Unique Link Clicks",
title = "Reach vs. Impressions \n(Bubble Size = Unique Link Clicks)")+
scale_colour_manual(values = c("Campaign 1"="#4B644B",
"Campaign 2"="#7D96AF",
"Campaign 3"="#F98B00",
"Campaign 4"="#09979B",
"Campaign 5"="#EA879C",
"Campaign 6"="#FDD835",
"Campaign 7"="#0070C0",
"Campaign 8"="#AE2B47",
"Campaign 9"="#EBA07E",
"Campaign 10"="#E06666",
"Campaign 11"="#002060"))+
theme(text = element_text(family = "Century Gothic", face = "bold",size = 14),
panel.background = element_blank(),
axis.line = element_line(colour = "black", size = 1),
axis.text = element_text(size = 15, colour = "black"),
legend.text = element_text(size = 14, colour = "black"),
plot.title = element_text(hjust = 0.5))
On the X-axis, we have Reach, which shows the number of unique users who
saw each campaign. On the Y-axis, we have Impressions, which is the
total number of times the ad was shown. Unique Link Clicks (ULC) was
used for the bubble size, which shows how many individuals clicked on
the ad. And, finally, each color represents a different campaign.
Top-right corner means the best visibility. Campaigns in the top-right
have high reach and high impressions, which implies strong visibility.
We find that the Campaign 2 (SHU_3 (Students Apart from India and US))
campaign and Campaign 7 (SHU_ Students (Nepal)) both had high reach and
impressions, making them stand out. Let’s analyze bubble size, which
translates to engagement: larger bubbles mean more people clicked on the
ad.
The Campaign 2 (SHU3 campaign) has a large bubble, suggesting high
engagement. Campaigns 4 and 11 (Canada and USA) have tiny bubbles,
indicating low click activity, even with modest reach. Campaigns
positioned in the lower-left corner of the graph represent poor
performers. These campaigns have low reach, low impressions, and small
bubble sizes. Some of these campaigns are Campaign 9 (SHU Students
(UAE)), Campaign 11 (SHU Students (USA)), and Campaign 4 (SHU Students
(Canada)), reflecting low visibility and weak engagement. Campaign 10,
the UK campaign is so insignificant in terms of Reach that it cannot
even be displayed.
Heatmaps
Visualizing how reach varies across campaigns and Age Range using a
heatmap.
campaign_age <- campaign_raw
campaign_age <- campaign_age |>
select(Age, campaign_id, Reach)
heatmap <- campaign_age |>
pivot_wider(id_cols = c(campaign_id),
names_from = "Age",
values_from = "Reach") |>
mutate(All_Ages= rowSums(across(c("13-17","18-24","25-34","35-44","45-54","55-64")), na.rm = TRUE))
heatmap <- heatmap |>
add_row(campaign_id="All Campaigns",`13-17`=sum(heatmap$`13-17`, na.rm = TRUE),`18-24`=sum(heatmap$`18-24`, na.rm = TRUE), `25-34`= sum(heatmap$`25-34`, na.rm = TRUE),
`35-44`=sum(heatmap$`35-44`, na.rm = TRUE), `45-54`=sum(heatmap$`45-54`, na.rm = TRUE), `55-64`=sum(heatmap$`55-64`, na.rm = TRUE), All_Ages=sum(heatmap$All_Ages, na.rm = TRUE))
heatmap_long <- heatmap |>
pivot_longer(-c(`campaign_id`),
names_to = "age",
values_to = "number")
# Replace NA values in dataframe with 0
heatmap_long[is.na(heatmap_long)] <- 0
heatmap_long |>
mutate(campaign_id = factor(campaign_id, levels = rev(c("All Campaigns","Campaign 2","Campaign 6","Campaign 7","Campaign 1","Campaign 8",
"Campaign 5","Campaign 9","Campaign 10","Campaign 4","Campaign 3","Campaign 11"))))|>
ggplot(aes(x = age, y = campaign_id, fill = ifelse(campaign_id == "All Campaigns" | age == "All_Ages", NA, number)))+
geom_tile()+
geom_text(aes(label = scales::comma(number)), family = "Century Gothic", size = 4.5)+
scale_fill_gradientn(colours = c("#FFFFCC","#C7E9B4","#7FCDBB","#41B6C4","#2C7FB8"), na.value = "#F2F1F1")+
labs(fill = "Reach", y = NULL, x = NULL, title = "Reach Across Campaigns by Age Group")+
scale_y_discrete(expand = c(0,0))+
scale_x_discrete(expand = c(0,0))+
theme(
text = element_text(family = "Century Gothic", face = "bold"),
axis.text = element_text(size = 13, colour = "black"),
axis.text.y = element_text(hjust = 1),
panel.grid.major = element_blank(),
plot.title = element_text(hjust = 0.5, size = 15))
According to this heatmap, across most campaigns, 18–24 is the strongest
performing age group. This is most prominent in Campaign 6, SHU_Students
(India) campaign, where we see a 30,110 reach. The second-strongest is
Campaign 2 (SHU3_ Students Apart from India and US)). But the older age
groups, ranging from 35 all the way to 64, show lighter shades, which
implies that campaigns are less effective among older audiences. The
only exception is Campaign 1 (SHU_6 (Educators and Principals)), which
reached 8,761 people aged 35–44, but it makes sense due to the target
audience. However, Campaign 10 (SHU_Students (UK)), Campaign 4
(SHU_Students (Canada)), and Campaign 3(SHU_Students (Australia)) have
low reach across all age groups, which is an important insight.
Pie/Donught Charts
Lets visualise how Reach is distributed across the different Age groups
using a donught
donut <- campaign_age |>
select(Age, Reach)
donut <- donut |>
group_by(Age)|>
summarise(Reach = sum(Reach))
donut <- donut |>
arrange(desc(Age)) |>
mutate(percent = Reach/sum(Reach)*100,
label = if_else(percent > 4, paste0(round(percent,1),"%"),""),
ypos = cumsum(Reach)- 0.5 * Reach )
donut |>
ggplot()+
geom_col(aes(x=2, y=Reach, fill = Age), width = 1)+
geom_text(aes(x=2,y = ypos, label = label), family = "Century Gothic", fontface = "bold", colour = "white",size = 7 )+
coord_polar(theta = "y")+
xlim(0.5, 2.5)+
scale_fill_manual(values = c("13-17"="#206095",
"18-24"="#F66068",
"25-34"="#22D0B6",
"35-44"="#871A5B",
"45-54"="#F98B00",
"55-64"="#002060"
))+
theme_void()+
labs(fill = "Age Group", title = "Campaign Reach Across Age groups")+
theme(legend.text = element_text(size = 12, family = "Century Gothic", face = "bold"),
text = element_text(family = "Century Gothic", face = "bold"),
plot.title = element_text(hjust = 0.5, size = 15))
Lets similarly visulaise the Audience (Geography) by the Reach
pie <- campaign_clean
pie <- pie |>
ungroup() |>
mutate(Reach = as.numeric(Reach))|>
arrange(desc(Geography))|>
mutate(percent = Reach/sum(Reach)*100,
label = if_else(percent > 3, paste0(scales::number(percent,accuracy = 0.1),"%"),""),
ypos = cumsum(Reach)- 0.5 * Reach)
pie |>
ggplot()+
geom_col(aes(x="",y = Reach, fill = Geography))+
coord_polar(theta = "y")+
geom_text(aes(x = "",y = ypos, label = label), family = "Century Gothic", size = 6.5, fontface = "bold",colour = "white")+
theme_void()+
labs(fill = "Geography", title = "Campaign Reach by Geographic Locations")+
guides(fill = guide_legend(reverse = FALSE))+
scale_fill_manual(values = c("Group 1"="#4B644B",
"Group 2"="#7D96AF",
"Australia"="#F98B00",
"Canada"="#09979B",
"Ghana"="#EA879C",
"India"="#FDD835",
"Nepal"="#0070C0",
"Nigeria"="#AE2B47",
"UAE"="#EBA07E",
"UK"="#E06666",
"USA"="#002060"))+
theme(text = element_text(family = "Century Gothic", size = 15, face = "bold"),
plot.title = element_text(hjust = 0.5, size = 15))
Group 1 (Australia, Canada, United Kingdom, Ghana, Nigeria, Pakistan,
United States) had the highest reach (24.6%) compared to the other
geographical locations followed by India with 16.9% of the total reach
of all campaigns.
Stacked Bar Chart
Let’s visualize how CTR is split among Age Ranges within each campaign.
ctrage <- campaign_raw |>
select(campaign_id, Age, Impressions, Clicks)
ctrage <- ctrage |>
group_by(Age, campaign_id)|>
summarise(Impressions = sum(Impressions),
Clicks = sum(Clicks))|>
mutate(cltr = Clicks/Impressions*100)
ctrage_wide <- ctrage |>
select(-Clicks, -Impressions) |>
pivot_wider(campaign_id,
names_from = Age,
values_from = cltr)
ctrage_long <- ctrage_wide |>
pivot_longer(-c(campaign_id),
names_to = "Age",
values_to = "ctr_rate")
ctrage_long |>
mutate(campaign_id = factor(campaign_id, levels = c("Campaign 8", "Campaign 11","Campaign 2","Campaign 4",
"Campaign 3","Campaign 1","Campaign 10","Campaign 5","Campaign 6","Campaign 9","Campaign 7")))|>
ggplot(aes(x = campaign_id, y = ctr_rate, fill = Age))+
geom_col(width = 0.8)+
geom_text(aes(label = if_else(ctr_rate > 0 ,paste0(round(ctr_rate,1), "%"),"")),
position = position_stack(vjust = 0.5), family = "Century Gothic", fontface = "bold", color = "white", size = 5)+
scale_fill_manual(values = c("13-17"="#206095",
"18-24"="#F66068",
"25-34"="#22D0B6",
"35-44"="#871A5B",
"45-54"="#F98B00",
"55-64"="#002060"
)) +
labs(x="Campaign Name", title = "Click-Through Rate Among Age Group Across Campaigns",
y = "Click Through Rate (CTR) ")+
scale_y_continuous(expand = c(0,0))+
scale_x_discrete(labels = scales::wrap_format(width = 5))+
theme(text = element_text(family = "Century Gothic", face = "bold", size = 14),
panel.background = element_blank(),
panel.grid.major.y = element_line(colour = "gray",size = 0.25),
axis.text.x = element_text(size = 12, colour = "black"),
axis.text.y = element_text(size = 14, colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
Campaign 8 (SHU_Students (Nigeria)) has the highest click-through rate
(CTR), with the 18-24 and 25-34 age groups making up the majority.
Campaign 11 (SHU_Students (USA)) comes second, also dominated by the
18-24 age group. Campaign 2 (SHU_3 (Students Apart from India and US))
follows, with a 17.8% CTR, again heavily targeting younger age groups.
Once again, Campaign 1(SHU 6 (Educators and Principals))cstands out as
the only campaign with a significant share from older age groups like
35-44, 45-54, and 55-64. Campaigns 5, 10, 9, 6 & 7 (*SHU_Students
(Ghana), UK, UAE, India, and Nepal) have very low overall CTR. These
campaigns aren’t contributing meaningfully to the total engagement and
may need to be reevaluated or redesigned. Based on the color
distributions, we can highlight that younger audiences (13–34) are the
primary contributors to reach across nearly all student campaigns. The
educator campaign is well-segmented, showing success in reaching an
older demographic. We can keep or expand these based on the company’s
goals.
KPI Analysis Build up
Let’s compare Click-Through Rate (CTR) across different geographies
geo_ctr <- campaign_clean
geo_ctr |>
ggplot(aes(x = reorder(Geography,CTR), y = CTR))+
geom_col(width = 0.9, fill = "#871A5B")+
geom_text(aes(label = paste0(scales::number(CTR, accuracy = 0.1),"%"), vjust = -1),
family="Century Gothic", fontface = "bold", size = 7)+
scale_y_continuous(expand = c(0,0),
labels = scales::number_format(accuracy = 1),
limits = nicelimits)+
labs(x = NULL, y = "Click-Through Rate (%)", title = "Click-Through Rate Across Geographic locations")+
theme(text = element_text(family = "Century Gothic", face = "bold", size = 14),
panel.background = element_blank(),
panel.grid.major.y = element_line(colour = "gray",size = 0.25),
axis.text.x = element_text(size = 15, colour = "black"),
axis.text.y = element_text(size = 15, colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
What we can infer from this chart is that Nigeria has the highest
click-through rate (CTR) among all geographies. USA and Group
2(Australia, Canada, United Kingdom, Ghana, Niger, Nigeria, Nepal,
Pakistan, Thailand, Taiwan) follow with a CTR of 6.1% and 5.6%
respectively. Canada, India, Australia, Ghana and the UK contribute
similar CTRs, each falling within the range of 3–4%. On the other hand
UAE, Group 1(Australia, Canada, United Kingdom, Ghana, Nigeria,
Pakistan, United States) and Nepal show lower CTRs, ranging from 2.2 -
2.8%.
Nigeria and USA appear to be the most cost-effective and
efficient regions in terms of campaign engagement, achieving higher
CTRs. These insights suggest that optimizing efforts in high CTR regions
or reevaluating strategies in lower CTR geographies like UAE and Nepal
might yield better results.
Let’s now shift our focus to
assess the same distribution but using uCTR as the metric.
geo_uctr <- campaign_clean
geo_uctr |>
ggplot(aes(x = reorder(campaign_id,uCTR), y = uCTR))+
geom_col(width = 0.9, fill = "#09979B")+
geom_text(aes(label = paste0(scales::number(uCTR, accuracy = 0.1),"%"), vjust = -1),
family="Century Gothic", fontface = "bold", size = 6)+
scale_y_continuous(expand = c(0,0),
labels = scales::number_format(accuracy = 1),
limits = nicelimits)+
labs(x = NULL, y = "Unique Click-Through Rate (%)", title = "Unique Click-Through Rate Across Geographic Locations")+
theme(text = element_text(family = "Century Gothic", face = "bold", size = 14),
panel.background = element_blank(),
panel.grid.major.y = element_line(colour = "gray",size = 0.25),
axis.text.x = element_text(size = 13, colour = "black"),
axis.text.y = element_text(size = 14, colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
The unique Click-Through Rate (uCTR), calculated as (Unique Clicks /
Reach) * 100, indicates the percentage of individuals who engaged with
the campaign out of the total audience reached. A lower uCTR reflects
weaker engagement. Campaign 9 (The SHU Students (UAE)) campaign recorded
the lowest uCTR, suggesting it had the least effective audience
engagement among all campaigns.
cpc_campaign <- campaign_clean
cpc_campaign |>
ggplot(aes(x = reorder(campaign_id,CPC), y = CPC))+
geom_col(width = 0.8, fill = "tan")+
coord_flip(clip = "off")+
geom_text(aes(label = paste0(round(CPC,2),"$")), hjust = -0.2,
family= "Century Gothic", fontface = "bold", colour = "black", size = 6)+
scale_y_continuous(expand = c(0,0),
limits = c(0,8.5))+
scale_x_discrete(labels = scales::wrap_format(width = 20))+
labs(x = NULL,
y = "Cost Per Click ($)", title = "Cost Per Click Across Campaigns")+
theme(text = element_text(family = "Century Gothic", face = "bold", colour = "black"),
panel.background = element_blank(),
panel.grid.major.x = element_line(colour = "gray",size = 0.25),
axis.text.y = element_text(size = 14, colour = "black"),
axis.text.x = element_text(size = 13, colour = "black"),
axis.title = element_text(size = 14, colour = "black", vjust = -2),
plot.title = element_text(hjust = 0.5, size = 15))
CPC is a metric that shows how much you pay for each click on your ad.
It is calculated by dividing the total amount spent by the number of
clicks received. CPC helps measure the efficiency of your ad spend. A
lower CPC means you’re getting more clicks for less money, while a
higher CPC may suggest your ad needs improvement.
ulc_amount <- campaign_clean
ulc_amount |>
ggplot(aes(x = ULC, y = amount_spent, fill = campaign_id))+
geom_point(size = 8, shape = 21) +
scale_fill_manual(values = c("Campaign 1"="#4B644B",
"Campaign 2"="#7D96AF",
"Campaign 3"="#EBA07E",
"Campaign 4"="#09979B",
"Campaign 5"="#EA879C",
"Campaign 6"="#FDD835",
"Campaign 7"="#0070C0",
"Campaign 8"="#AE2B47",
"Campaign 9"="#FFFFCC",
"Campaign 10"="#E06666",
"Campaign 11"="#002060"))+
labs(x = "Unique Link Clicks (ULC)",
y = "Total Amount SPent ($)", title = "Unique Link Clicks vs Amount Spent on Campaign",
fill = NULL)+
scale_y_continuous(
limits = c(500,3000))+
theme(text = element_text(family = "Century Gothic", face = "bold", colour = "black", size = 16),
panel.background = element_blank(),
axis.line = element_line(colour = "gray", size = 0.5),
axis.text= element_text(size = 18),
legend.background = element_rect(colour = "black"),
plot.title = element_text(hjust = 0.5, size = 15))
This metric highlights the cost-efficiency of each campaign by comparing
total amount spent to the number of unique link clicks (ULC). Campaigns
with high spend but relatively low ULC such as Campaign SHU 6 (Educators
and Principals) indicate lower efficiency and may require strategic
adjustments. In contrast, campaigns with high ULC and moderate spend
reflect stronger performance and better return on investment.
ctr_reach <- campaign_clean
ctr_reach <- ctr_reach |>
mutate(vjust_pos = if_else(campaign_id == "Campaign 9",1.8,-0.9))
ctr_reach |>
ggplot(aes(x = Reach, y = CTR, colour = campaign_id))+
geom_point(size = 4)+
geom_text(aes(label = str_wrap(campaign_id, width = 34), vjust = vjust_pos),
family = "Century Gothic", fontface = "bold", size = 4)+
scale_y_continuous(expand = c(0,1))+
scale_x_continuous(limits = nicelimits,
labels = scales::comma)+
scale_colour_manual(values = c("Campaign 1"="#4B644B",
"Campaign 2"="#7D96AF",
"Campaign 3"="#EBA07E",
"Campaign 4"="#09979B",
"Campaign 5"="#EA879C",
"Campaign 6"="#FDD835",
"Campaign 7"="#0070C0",
"Campaign 8"="#AE2B47",
"Campaign 9"="#F98B00",
"Campaign 10"="#871A5B",
"Campaign 11"="#002060"))+
labs(x = "Reach", y = "CTR(%)", title = "Click-Through Rate vs Reach")+
theme(text = element_text(family = "Century Gothic", face = "bold"),
panel.background = element_blank(),
axis.line = element_line(colour = "grey", size = 0.5),
axis.text = element_text(size = 15),
axis.title = element_text(size = 15),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 15))
Campaign 7 (SHU_Students(Nepal)) despite having more reach has the
lowest CTR of all the campaigns.
cpc_ctr <- campaign_clean
cpc_ctr <- cpc_ctr|>
mutate(vjust_pos = if_else(campaign_id == "Campaign 10",1.5,-0.6))
cpc_ctr |>
ggplot(aes(x=CTR, y=CPC, colour = campaign_id))+
geom_point(size = 4)+
geom_text(aes(label = str_wrap(campaign_id, width = 40), vjust = vjust_pos),
family = "Century Gothic", fontface = "bold", size = 6)+
scale_y_continuous(expand = c(0,1))+
scale_x_continuous(limits = nicelimits,
labels = scales::comma)+
scale_colour_manual(values = c("Campaign 1"="#4B644B",
"Campaign 2"="#7D96AF",
"Campaign 3"="#EBA07E",
"Campaign 4"="#09979B",
"Campaign 5"="#EA879C",
"Campaign 6"="#FDD835",
"Campaign 7"="#0070C0",
"Campaign 8"="#AE2B47",
"Campaign 9"="#F98B00",
"Campaign 10"="#871A5B",
"Campaign 11"="#002060"))+
labs(x = "Click Through Rate(%)", y = "Cost Per Click($)", title = "Cost Per Click vs Click-Through Rate")+
theme(text = element_text(family = "Century Gothic", face = "bold"),
panel.background = element_blank(),
axis.line = element_line(colour = "grey", size = 1),
axis.text = element_text(size = 20),
axis.title = element_text(size = 20),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 15))
The most efficient campaigns with high CTR and Low CPC are Campaing 8
(SHU_Students (Nigeria)) and Campaign 2 (SHU3_ (Students Apart from
India and US)). Both of them show a very high CTR, and extremely low
CPC, around 1 INR. Thus, these campaigns are high-performing and
cost-effective. Similarly, the least efficient campaigns, with low CTR
and high CPC are Campaign 3 (SHU_Students (Australia)) and Campaign 10
(SHU_Students (UK)). These have low to moderate CTR and an extremely
high CPC. These are costly and less engaging campaigns, and possibly not
worth the investment.
Conclusion - KPI Initial Analysis
Based on a thorough performance analysis, dropping the SHU_Students
(Australia) campaign is recommended. It has the highest Cost Per Click
(CPC) at 7.15$ and its Click-Through Rate (CTR) is low at 3.3%. Given
its high CPC and high spend (850$), the campaign may not be delivering
proportional value and should be dropped.
Supporting Evidence:
-
Highest Cost Per Click (CPC): At $7.15, Australia’s CPC
is:
-
608% higher than the average campaign CPC ($1.01)
-
21x higher than the top-performing Nigeria campaign ($0.34)
-
Second-worst efficiency score in our multi-factor analysis
-
Suboptimal Engagement:
-
3.3% CTR (below the 4.2% campaign average)
-
Despite significant reach (14,492), conversion metrics remain poor
-
Underperforms compared to similar markets (UK, USA)
-
Budget Impact:
-
Immediate cost savings: $850
-
Represents 14% of total marketing budget
-
High reallocation potential to better-performing campaigns
-
Financial Impact:
Discontinuing the Australia
campaign will:
The deeper shaded bar shows the campaign with the highest CPC out of
all.
Key Insight: The Australian campaign, thus
Campaign 3’s CPC is approximately 608% higher than the
average campaign CPC, demonstrating severe cost inefficiency. Let’s
visualize this;
avg <- campaign_clean
Average_CPC = sum(campaign_clean$amount_spent)/sum(campaign_clean$Clicks)
avg_cpc <- tribble(~ind, ~value,
"Average Campaign CPC", Average_CPC,
"Campaign 3", as.numeric(campaign_clean[campaign_clean$campaign_id=="Campaign 3","CPC"]))
avg_cpc |>
ggplot(aes(x = ind, y = value, fill = ind))+
geom_col(width = 0.8)+
geom_text(aes(label = paste0(scales::number(value, accuracy = 0.01),"$")),
family = "Times New Roman", fontface = "bold", size = 6, vjust = -0.5)+
geom_text(aes(label = ifelse(value > 5, paste0("608% higher ▲ \n"),"")), vjust = -0.1,
family = "Times New Roman", fontface = "bold",size = 6.5, colour = "#871A5B")+
scale_y_continuous(expand = c(0,0), limits = c(0,8))+
scale_fill_manual(values = c("Average Campaign CPC"= "#22D0B6",
"Campaign 3"= "#871A5B")) +
labs(fill = NULL, x = NULL, y = "Cost Per Click", title = "Cost Per Click Comparison: Campaign Average vs Campaign 3")+
theme(text = element_text(family = "Century Gothic", colour = "black", face = "bold", size = 13),
axis.text = element_text(size = 18, colour = "black"),
panel.background = element_blank(),
panel.grid.major.y = element_line(size = 0.25, colour = "gray"),
plot.title = element_text(hjust = 0.5),
legend.position = "none")
This graph compares the average campaign CPC with Campaign 3 (Australian
campaign)’s CPC. The difference is clearly visible in this graph. When
benchmarked against the most cost-efficient campaign (Campaign 8) which
had a CPC of 0.34\(, the contrast becomes even
more
dramatic:</p>
<div class="sourceCode" id="cb20"><pre
class="sourceCode r"><code class="sourceCode
r"><span id="cb20-1"><a
href="#cb20-1" tabindex="-1"></a>avg_1
<span class="ot"><-</span>
campaign_clean</span>
<span id="cb20-2"><a href="#cb20-2"
tabindex="-1"></a></span>
<span id="cb20-3"><a href="#cb20-3"
tabindex="-1"></a>avg1_cpc <span
class="ot"><-</span> <span
class="fu">tribble</span>(<span
class="sc">~</span>ind, <span
class="sc">~</span>value,</span>
<span id="cb20-4"><a href="#cb20-4"
tabindex="-1"></a> <span
class="st">"Campaign 3"</span>,
<span
class="fu">as.numeric</span>(campaign_clean[campaign_clean<span
class="sc">\)campaign_id =="Campaign 3" , "CPC"]), "Campaign 8", as.numeric(campaign_clean[campaign_clean\(</span>campaign_id <span
class="sc">==</span><span
class="st">"Campaign
8"</span>,<span
class="st">"CPC"</span>]))</span>
<span id="cb20-6"><a href="#cb20-6"
tabindex="-1"></a>avg1_cpc <span
class="sc">|></span></span>
<span id="cb20-7"><a href="#cb20-7"
tabindex="-1"></a> <span
class="fu">ggplot</span>(<span
class="fu">aes</span>(<span
class="at">x =</span> <span
class="fu">reorder</span>(ind,value), <span
class="at">y =</span> value, <span
class="at">fill =</span> ind))<span
class="sc">+</span></span>
<span id="cb20-8"><a href="#cb20-8"
tabindex="-1"></a> <span
class="fu">geom_col</span>(<span
class="at">width =</span> <span
class="fl">0.8</span>)<span
class="sc">+</span></span>
<span id="cb20-9"><a href="#cb20-9"
tabindex="-1"></a> <span
class="fu">geom_text</span>(<span
class="fu">aes</span>(<span
class="at">label =</span> <span
class="fu">paste0</span>(scales<span
class="sc">::</span><span
class="fu">number</span>(value, <span
class="at">accuracy =</span> <span
class="fl">0.01</span>),<span
class="st">"\)")), family = "Times New Roman",
fontface = "bold", size = 7, vjust = -0.5)+ geom_text(aes(label = ifelse(value > 5, paste0("23.3x higher ▲ "),"")), vjust = -0.1, family = "Times New Roman",
fontface = "bold",size = 7, colour = "#871A5B")+ scale_y_continuous(expand =
c(0,0), limits = c(0,8))+ scale_fill_manual(values =
c("Campaign 8"= "#F98B00", "Campaign 3"= "#871A5B")) + labs(fill = NULL, x = NULL, y = "Cost Per Click", title =
"Cost Per Click Comparison: Campaign 3 vs Campaign
8")+ theme(text = element_text(family = "Century Gothic", colour =
"black", face = "bold", size = 13), axis.text = element_text(size = 18, colour = "black"),
panel.background = element_blank(), panel.grid.major.y = element_line(size = 0.25, colour = "gray"), plot.title = element_text(hjust = 0.5), legend.position = "none")]), "Other
Campaigns", sum(campaign_clean\(</span>amount_spent)<span
class="sc">-</span><span
class="fu">as.numeric</span>(campaign_clean[campaign_clean<span
class="sc">\)campaign_id=="Campaign 3","amount_spent"])) reallocation |> ggplot(aes(x
= name, y = value, fill
= name))+ geom_col(width = 0.8)+ geom_text(aes(label = paste0("\("</span>,
scales<span class="sc">::</span><span
class="fu">comma</span>(value,<span
class="at">accuracy =</span> <span
class="fl">0.01</span>))), </span>
<span id="cb27-9"><a href="#cb27-9"
tabindex="-1"></a> <span
class="at">family=</span><span
class="st">"Century Gothic"</span>,
<span class="at">fontface =</span> <span
class="st">"bold"</span>, <span
class="at">size =</span> <span
class="dv">6</span>, <span
class="at">vjust =</span> <span
class="sc">-</span><span
class="dv">1</span>)<span
class="sc">+</span></span>
<span id="cb27-10"><a href="#cb27-10"
tabindex="-1"></a> <span
class="fu">scale_y_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>), <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb27-11"><a href="#cb27-11"
tabindex="-1"></a> <span
class="fu">scale_fill_manual</span>(<span
class="at">values =</span> <span
class="fu">c</span>(<span
class="st">"Campaign
3"</span><span
class="ot">=</span><span
class="st">"#871A5B"</span>,</span>
<span id="cb27-12"><a href="#cb27-12"
tabindex="-1"></a> <span
class="st">"Other
Campaigns"</span><span
class="ot">=</span><span
class="st">"#04BCFC"</span>))<span
class="sc">+</span></span>
<span id="cb27-13"><a href="#cb27-13"
tabindex="-1"></a> <span
class="fu">labs</span>(<span
class="at">x=</span><span
class="cn">NULL</span>, <span
class="at">y=</span><span
class="st">"Budget (\))", title = "Reallocation Potential:
Dropping Campaign 3")+ theme(text = element_text(family = "Century Gothic", face = "bold", size = 13), panel.background = element_blank(), panel.grid.major.y = element_line(size = 0.25, color = "gray"), axis.text.x = element_text(size = 15, colour = "black"), legend.position = "none", plot.title = element_text(hjust = 0.5))
Efficiency Projection
eff_projection <- campaign_clean
current_cpc <- sum(eff_projection$amount_spent)/sum(eff_projection$Clicks)
eff_projection_2 <- eff_projection |>
filter(campaign_id != "Campaign 3")
improved_cpc <- sum(eff_projection_2$amount_spent)/sum(eff_projection_2$Clicks)
eff_proj <- tribble(~name,~value,
"Current Portfolio CPC", current_cpc,
"Improved Portfolio CPC", improved_cpc)
eff_proj |>
ggplot(aes(x = name, y = value, fill = name))+
geom_col(width = 0.8)+
geom_segment(aes(x = "Current Portfolio CPC", xend = "Improved Portfolio CPC", y = current_cpc, yend = improved_cpc),
arrow = arrow(length = unit(0.3, "cm")),
size = 1,
colour = "purple", linetype = "solid")+
annotate("text", x= 1, y = 1, label = "6% Improvement", hjust = -1, family = "Century Gothic", fontface = "bold", colour = "purple", size = 4.5)+
geom_text(aes(label = paste0("$", round(value,2)), vjust = -1),
family = "Century Gothic", fontface = "bold", size = 6)+
scale_fill_manual(values = c("Current Portfolio CPC"="lightblue", "Improved Portfolio CPC"="lightgreen"))+
scale_y_continuous(expand = c(0,0), limits = nicelimits)+
labs(x=NULL, y="Cost Per Click", title = "Overall Campaign Efficiency Improvement")+
theme(panel.background = element_blank(),
panel.grid.major.y = element_line(colour = "gray",size = 0.25),
axis.text = element_text(colour = "black", size = 15, family = "Century Gothic", face = "bold"),
axis.title = element_text(colour = "black", size = 15, family = "Century Gothic", face = "bold"),
legend.position = "none",
plot.title = element_text(colour = "black", size = 15, family = "Century Gothic", face = "bold", hjust = 0.5))
Recommendations
Dropping the Campaign 3 allows the client to focus
their attention on more profitable campaigns. While the Campaign
10 is also a financial burden, we believe that it can be
optimized. However, we see that Campaign 8, with a
CTR of 9.5% and CPC
of just \(0.34</strong>, comes
up as the top performer in both
engagement and cost-efficiency. Let’s visualize these metrics.</p>
<div class="sourceCode" id="cb29"><pre
class="sourceCode r"><code class="sourceCode
r"><span id="cb29-1"><a
href="#cb29-1" tabindex="-1"></a><span
class="co">#campaign performance 1: cpc vs
ctr</span></span>
<span id="cb29-2"><a href="#cb29-2"
tabindex="-1"></a></span>
<span id="cb29-3"><a href="#cb29-3"
tabindex="-1"></a>cp1 <span
class="ot"><-</span>
campaign_clean</span>
<span id="cb29-4"><a href="#cb29-4"
tabindex="-1"></a></span>
<span id="cb29-5"><a href="#cb29-5"
tabindex="-1"></a>cp1 <span
class="ot"><-</span> cp1 <span
class="sc">|></span></span>
<span id="cb29-6"><a href="#cb29-6"
tabindex="-1"></a> <span
class="fu">mutate</span>(<span
class="at">highlight =</span> campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
8"</span>)</span>
<span id="cb29-7"><a href="#cb29-7"
tabindex="-1"></a></span>
<span id="cb29-8"><a href="#cb29-8"
tabindex="-1"></a>cp1<span
class="sc">|></span></span>
<span id="cb29-9"><a href="#cb29-9"
tabindex="-1"></a> <span
class="fu">ggplot</span>(<span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC, <span
class="at">colour =</span> highlight))<span
class="sc">+</span></span>
<span id="cb29-10"><a href="#cb29-10"
tabindex="-1"></a> <span
class="fu">geom_point</span>(<span
class="at">size =</span> <span
class="dv">4</span>)<span
class="sc">+</span></span>
<span id="cb29-11"><a href="#cb29-11"
tabindex="-1"></a> <span
class="fu">geom_text</span>(<span
class="at">data =</span> <span
class="fu">subset</span>(cp1, campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
8"</span>),</span>
<span id="cb29-12"><a href="#cb29-12"
tabindex="-1"></a> <span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC), <span
class="at">label =</span> <span
class="st">"Top Performer"</span>,
<span class="at">show.legend =</span> <span
class="cn">FALSE</span>,</span>
<span id="cb29-13"><a href="#cb29-13"
tabindex="-1"></a> <span
class="at">family =</span> <span
class="st">"Century Gothic"</span>,
<span class="at">vjust =</span> <span
class="sc">-</span><span
class="dv">1</span>, <span
class="at">hjust =</span> <span
class="fl">0.69</span>, <span
class="at">size =</span> <span
class="dv">5</span>, <span
class="at">fontface =</span> <span
class="st">"bold"</span>)<span
class="sc">+</span></span>
<span id="cb29-14"><a href="#cb29-14"
tabindex="-1"></a> <span
class="fu">scale_color_manual</span>(<span
class="at">values =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"grey"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span> <span
class="st">"#E74C3C"</span>),</span>
<span id="cb29-15"><a href="#cb29-15"
tabindex="-1"></a> <span
class="at">labels =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"Other
Campaigns"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span><span
class="st">"Campaign
8"</span>))<span
class="sc">+</span></span>
<span id="cb29-16"><a href="#cb29-16"
tabindex="-1"></a> <span
class="fu">scale_x_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb29-17"><a href="#cb29-17"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb29-18"><a href="#cb29-18"
tabindex="-1"></a> <span
class="fu">scale_y_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb29-19"><a href="#cb29-19"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb29-20"><a href="#cb29-20"
tabindex="-1"></a> <span
class="fu">labs</span>(<span
class="at">x =</span> <span
class="st">"Click Through
Rate(%)"</span>, <span class="at">y
=</span> <span class="st">"Cost Per
Click(\))", title = "Campaign Performance: CTR vs CPC", colour = NULL)+ theme(text = element_text(family = "Century Gothic", face = "bold", size = 13), panel.background = element_blank(), axis.line = element_line(colour = "grey", size = 1), axis.text = element_text(size = 15), axis.title = element_text(size = 15), plot.title = element_text(hjust = 0.5, size = 15))
Campaign 8 stands ahead of all the other campaigns.
However, the campaign with an excellent balance is Campaign
2 with a CTR of 5.6% and a
CPC of only \(0.42</strong>. This implies it is
a very efficient spend. Let’s also visualize this;</p>
<div class="sourceCode" id="cb30"><pre
class="sourceCode r"><code class="sourceCode
r"><span id="cb30-1"><a
href="#cb30-1" tabindex="-1"></a><span
class="co">#campaign performance 2: cpc vs
ctr</span></span>
<span id="cb30-2"><a href="#cb30-2"
tabindex="-1"></a>cp2 <span
class="ot"><-</span>
campaign_clean</span>
<span id="cb30-3"><a href="#cb30-3"
tabindex="-1"></a></span>
<span id="cb30-4"><a href="#cb30-4"
tabindex="-1"></a>cp2 <span
class="ot"><-</span> cp2 <span
class="sc">|></span></span>
<span id="cb30-5"><a href="#cb30-5"
tabindex="-1"></a> <span
class="fu">mutate</span>(<span
class="at">highlight =</span> campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
2"</span>)</span>
<span id="cb30-6"><a href="#cb30-6"
tabindex="-1"></a></span>
<span id="cb30-7"><a href="#cb30-7"
tabindex="-1"></a>cp2<span
class="sc">|></span></span>
<span id="cb30-8"><a href="#cb30-8"
tabindex="-1"></a> <span
class="fu">ggplot</span>(<span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC, <span
class="at">colour =</span> highlight))<span
class="sc">+</span></span>
<span id="cb30-9"><a href="#cb30-9"
tabindex="-1"></a> <span
class="fu">geom_point</span>(<span
class="at">size =</span> <span
class="dv">4</span>)<span
class="sc">+</span></span>
<span id="cb30-10"><a href="#cb30-10"
tabindex="-1"></a> <span
class="fu">geom_text</span>(<span
class="at">data =</span> <span
class="fu">subset</span>(cp2, campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
2"</span>),</span>
<span id="cb30-11"><a href="#cb30-11"
tabindex="-1"></a> <span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC), <span
class="at">label =</span> <span
class="st">"Efficient
Spend"</span>, <span
class="at">show.legend =</span> <span
class="cn">FALSE</span>,</span>
<span id="cb30-12"><a href="#cb30-12"
tabindex="-1"></a> <span
class="at">family =</span> <span
class="st">"Century Gothic"</span>,
<span class="at">vjust =</span> <span
class="sc">-</span><span
class="dv">1</span>, <span
class="at">hjust =</span> <span
class="fl">0.69</span>, <span
class="at">size =</span> <span
class="dv">5</span>, <span
class="at">fontface =</span> <span
class="st">"bold"</span>)<span
class="sc">+</span></span>
<span id="cb30-13"><a href="#cb30-13"
tabindex="-1"></a> <span
class="fu">scale_color_manual</span>(<span
class="at">values =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"grey"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span> <span
class="st">"#22D0B6"</span>),</span>
<span id="cb30-14"><a href="#cb30-14"
tabindex="-1"></a> <span
class="at">labels =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"Other
Campaigns"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span><span
class="st">"Campaign
2"</span>))<span
class="sc">+</span></span>
<span id="cb30-15"><a href="#cb30-15"
tabindex="-1"></a> <span
class="fu">scale_x_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb30-16"><a href="#cb30-16"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb30-17"><a href="#cb30-17"
tabindex="-1"></a> <span
class="fu">scale_y_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb30-18"><a href="#cb30-18"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb30-19"><a href="#cb30-19"
tabindex="-1"></a> <span
class="fu">labs</span>(<span
class="at">x =</span> <span
class="st">"Click Through
Rate(%)"</span>, <span class="at">y
=</span> <span class="st">"Cost Per
Click(\))", title = "Campaign Performance: CTR vs CPC", colour = NULL)+ theme(text = element_text(family = "Century Gothic", face = "bold", size = 13), panel.background = element_blank(), axis.line = element_line(colour = "grey", size = 1), axis.text = element_text(size = 15), axis.title = element_text(size = 15), plot.title = element_text(hjust = 0.5, size = 15))
Campaign 2 sits perfectly in the lower middle part of
the chart, which makes it not only an efficient spend, but ready for
future optimizations. There’s another campaign that, despite having a
larger CPC, has a higher CTR and, thus, stronger engagement. It’s
Campaign 11, with a CTR of
6.1% and a moderate CPC of
\(5.04</strong>. Strong
engagement despite a slightly higher cost
per click. Let’s visualize this;</p>
<div class="sourceCode" id="cb31"><pre
class="sourceCode r"><code class="sourceCode
r"><span id="cb31-1"><a
href="#cb31-1" tabindex="-1"></a><span
class="co">#campaign performance 3: cpc vs
ctr</span></span>
<span id="cb31-2"><a href="#cb31-2"
tabindex="-1"></a>cp3 <span
class="ot"><-</span>
campaign_clean</span>
<span id="cb31-3"><a href="#cb31-3"
tabindex="-1"></a></span>
<span id="cb31-4"><a href="#cb31-4"
tabindex="-1"></a>cp3 <span
class="ot"><-</span> cp3 <span
class="sc">|></span></span>
<span id="cb31-5"><a href="#cb31-5"
tabindex="-1"></a> <span
class="fu">mutate</span>(<span
class="at">highlight =</span> campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
11"</span>)</span>
<span id="cb31-6"><a href="#cb31-6"
tabindex="-1"></a></span>
<span id="cb31-7"><a href="#cb31-7"
tabindex="-1"></a>cp3<span
class="sc">|></span></span>
<span id="cb31-8"><a href="#cb31-8"
tabindex="-1"></a> <span
class="fu">ggplot</span>(<span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC, <span
class="at">colour =</span> highlight))<span
class="sc">+</span></span>
<span id="cb31-9"><a href="#cb31-9"
tabindex="-1"></a> <span
class="fu">geom_point</span>(<span
class="at">size =</span> <span
class="dv">4</span>)<span
class="sc">+</span></span>
<span id="cb31-10"><a href="#cb31-10"
tabindex="-1"></a> <span
class="fu">geom_text</span>(<span
class="at">data =</span> <span
class="fu">subset</span>(cp2, campaign_id <span
class="sc">==</span> <span
class="st">"Campaign
11"</span>),</span>
<span id="cb31-11"><a href="#cb31-11"
tabindex="-1"></a> <span
class="fu">aes</span>(<span
class="at">x =</span> CTR, <span
class="at">y =</span> CPC), <span
class="at">label =</span> <span
class="st">"Strong
Engagement"</span>, <span
class="at">show.legend =</span> <span
class="cn">FALSE</span>,</span>
<span id="cb31-12"><a href="#cb31-12"
tabindex="-1"></a> <span
class="at">family =</span> <span
class="st">"Century Gothic"</span>,
<span class="at">vjust =</span> <span
class="sc">-</span><span
class="dv">1</span>, <span
class="at">hjust =</span> <span
class="fl">0.69</span>, <span
class="at">size =</span> <span
class="dv">5</span>, <span
class="at">fontface =</span> <span
class="st">"bold"</span>, <span
class="at">colour =</span><span
class="st">"blue"</span>)<span
class="sc">+</span></span>
<span id="cb31-13"><a href="#cb31-13"
tabindex="-1"></a> <span
class="fu">scale_color_manual</span>(<span
class="at">values =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"grey"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span> <span
class="st">"blue"</span>),</span>
<span id="cb31-14"><a href="#cb31-14"
tabindex="-1"></a> <span
class="at">labels =</span> <span
class="fu">c</span>(<span
class="st">"FALSE"</span><span
class="ot">=</span><span
class="st">"Other
Campaigns"</span>, <span
class="st">"TRUE"</span><span
class="ot">=</span><span
class="st">"Campaign
11"</span>))<span
class="sc">+</span></span>
<span id="cb31-15"><a href="#cb31-15"
tabindex="-1"></a> <span
class="fu">scale_x_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb31-16"><a href="#cb31-16"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb31-17"><a href="#cb31-17"
tabindex="-1"></a> <span
class="fu">scale_y_continuous</span>(<span
class="at">expand =</span> <span
class="fu">c</span>(<span
class="dv">0</span>,<span
class="dv">0</span>),</span>
<span id="cb31-18"><a href="#cb31-18"
tabindex="-1"></a> <span
class="at">limits =</span> nicelimits)<span
class="sc">+</span></span>
<span id="cb31-19"><a href="#cb31-19"
tabindex="-1"></a> <span
class="fu">labs</span>(<span
class="at">x =</span> <span
class="st">"Click Through
Rate(%)"</span>, <span class="at">y
=</span> <span class="st">"Cost Per
Click(\))", title = "Campaign Performance: CTR vs CPC", colour = NULL)+ theme(text = element_text(family = "Century Gothic", face = "bold", size = 13), panel.background = element_blank(), axis.line = element_line(colour = "grey", size = 1), axis.text = element_text(size = 15), axis.title = element_text(size = 15), plot.title = element_text(hjust = 0.5, size = 15))
|